13.(1 pt)For each of the following situations, indicate whether ANOVA is appropriate; if not appropriate, the reason why not; and, if appropriate, the type of ANOVA that would be used (i.e., one-way, repeated measures, etc.)

a.The IVs are ethnicity (Asian, White, African American, Hispanic) and gender (male vs female); the DV is serum cholesterol levels.

Appropriate; multi-level anova because the IV is multiple

b.The IV is smoking status - smokers vs non-smokers; the DV is a health-related hardiness score, measured on a 20-item scale.

Appropriate: two groups test would require a t-statistic

c.The IV is maternal breast feeding status (breastfeeds daily vs doesn't breastfeed); the DV is a maternal bonding with infant score, measured on a 20-item scale.

Appropriate; two way ANOVA

d.The IV is length of gestation (preterm vs term vs postterm) using the same multiple birth mothers over time; the DV is blood pressure 10 minutes post delivery.

Inappropriate: there are too many IVs

please help

calculate confidence intervals for the quantitative variables in the Heart Rate Dataset.

Steps

1. Open the Heart Rate Dataset in Excel and identify the quantitative variables
2. Make sure the data is sorted by category (e.g.male-at-rest,female at-rest,etc.)
3. Use the Data Analysis toolsof Excel to construct 95%and 99%confidence intervals for all 8 categories of the sorted quantitative variables.Excel will calculate the margin of error given as the "confidence interval" in the output. Use this margin of error to c a 8 confidence intervals by both adding and subtracting it from the sample mean (calculated in unit 3).This creates a range of values that is the confidence interval.
4. Create Word document, and use your calculated results to explain what the confidence intervals tell us. What do these confidence intervals tell us about our heart rate data?How would you interpret the 95% and 99% intervals for resting heart rate?
5. Compare the 99% and 95% intervals and explain why one is larger than the other even though we use the same sample mean value.

Heart rate before and after exercise

M=0 F=1 Resting After Exercise

0 55.0 65.0

0 59.0 68.2

0 60.0 78.1

0 60.0 95.7

0 60.1 79.0

0 60.3 82.7

0 60.4 97.6

0 60.8 86.7

0 62.0 88.0

0 64.0 70.7

0 65.0 75.0

0 65.3 86.3

0 67.0 79.9

0 67.4 78.8

0 67.7 79.4

0 68.6 72.8

0 69.2 81.4

0 69.2 79.4

0 69.4 82.6

0 69.4 74.1

0 70.0 90.7

0 70.0 100.0

0 70.0 83.0

0 70.0 80.0

0 70.9 82.0

0 71.0 87.0

0 71.0 80.1

0 71.6 81.5

0 72.0 93.9

0 72.3 80.9

0 72.8 86.7

0 73.1 71.9

0 73.4 82.7

0 73.9 85.2

0 73.9 89.1

0 74.2 82.1

0 74.2 88.8

0 74.3 85.6

0 74.3 89.2

0 74.5 75.6

0 74.5 88.0

0 74.6 86.7

0 74.8 90.2

0 74.8 94.0

0 74.8 81.3

0 74.8 83.7

0 75.2 86.5

0 75.2 84.9

0 75.3 84.1

0 75.4 84.8

0 75.4 93.1

0 75.5 84.4

0 75.5 80.2

0 75.6 84.3

0 76.0 88.7

0 76.0 90.8

0 76.1 84.1

0 76.2 89.9

0 76.3 85.9

0 76.3 87.0

0 76.4 88.0

0 76.6 83.7

0 76.6 86.4

0 76.7 91.8

0 76.8 83.3

0 76.8 92.8

0 76.9 92.3

0 76.9 87.7

0 77.0 84.7

0 77.2 85.8

0 77.6 84.6

0 77.6 80.6

0 77.7 89.9

0 77.8 97.8

0 77.9 83.8

0 77.9 99.0

0 77.9 91.6

0 78.0 89.8

0 78.1 87.2

0 78.2 86.0

0 78.2 94.0

0 78.3 91.1

0 78.4 87.2

0 78.4 85.5

0 78.4 88.0

0 78.5 89.9

0 78.5 98.5

0 78.6 87.0

0 78.8 90.4

0 78.8 90.9

0 78.9 89.7

0 78.9 90.7

0 78.9 91.4

0 79.0 91.6

0 79.2 90.4

0 79.4 91.0

0 79.5 88.6

0 79.8 81.5

0 79.8 88.2

0 80.0 90.2

0 80.0 89.1

0 80.1 94.6

0 80.2 83.3

0 80.3 93.4

0 80.3 88.0

0 80.4 90.8

0 80.4 89.2

0 80.5 95.3

1 80.5 87.4

1 80.5 101.3

1 80.6 85.2

1 80.6 95.9

1 80.8 84.2

1 81.2 90.7

1 81.3 97.7

1 81.4 100.9

1 81.5 84.2

1 81.5 86.5

1 81.6 90.3

1 81.8 93.8

1 81.9 97.5

1 82.1 85.6

1 82.1 93.5

1 82.3 86.9

1 82.4 89.3

1 82.5 95.1

1 82.7 89.1

1 82.8 93.1

1 82.9 90.2

1 82.9 91.9

1 82.9 85.9

1 83.0 90.0

1 83.2 93.0

1 83.2 94.5

1 83.3 87.7

1 83.3 82.1

1 83.4 97.4

1 83.7 90.5

1 83.9 93.9

1 84.0 90.4

1 84.2 87.9

1 84.4 96.7

1 84.6 93.0

1 84.7 90.9

1 84.9 96.0

1 84.9 95.1

1 85.0 100.0

1 85.2 97.7

1 85.2 93.5

1 85.2 89.7

1 85.2 99.1

1 85.3 94.2

1 85.5 97.4

1 85.6 96.2

1 85.8 90.4

1 85.8 90.5

1 86.2 95.9

1 86.2 98.9

1 86.3 99.7

1 86.3 99.7

1 86.4 100.6

1 86.6 90.6

1 86.9 95.0

1 87.1 95.9

1 87.3 94.1

1 87.4 103.6

1 87.4 91.7

1 87.5 105.9

1 87.5 93.9

1 87.7 95.1

1 87.7 98.0

1 87.8 98.5

1 88.0 94.2

1 88.1 102.1

1 88.3 90.1

1 88.3 90.5

1 88.4 103.0

1 88.7 97.1

1 88.8 98.8

1 89.2 96.9

1 89.4 96.7

1 89.4 99.2

1 89.7 94.8

1 89.7 94.3

1 89.8 97.4

1 89.8 92.9

1 90.2 96.4

1 90.5 101.2

1 91.2 100.6

1 91.4 100.9

1 91.7 99.2

1 92.3 102.2

1 92.4 101.2

1 92.8 99.8

1 93.2 89.4

1 94.4 101.9

1 96.1 100.2

1 96.5 99.3

1 97.0 104.5

1 97.3 103.3

As a manager of a small business, you are considering to introduce a new product. The production requires a new machine. You figure out that you could buy it for $190,000, but the price could be in between $180,000 and $200,000. Because of the budget limitation you can only pay 60% of the machine price with your own saving.You will borrow the other 40% with an interest rate around 9% per year (but subject to change in between 8.5% and 10%).

The demand of this product is predicted to be 15,000 per year and but could be in between 14800 and 15500. The unit price could be in between $2 and $3, and now you believe that $2.5 is a reasonable price right now. The raw material cost is estimated to be $0.9 but could be in between $0.5 and $1.2. The operation cost of the equipment is around $0.2 for one product but could be in between $0.1 and $0.25.The maintenance cost for this equipment is estimated to be $2000 per year but could be in between $1500 and $2300.

Suppose you could always invest your cash in the money market that give a return at 8% per year for sure.

Please do following analysis:

a)Construct the Influence Diagram for this decision problem and identify the inputs and mathematic models that relate these inputs (decision variables) to the decision problem. (10)

b)Construct the Tornado Diagram (10)

c)Based on Tornado Diagram, identify two most sensitive variables, and do two-way sensitivity analysis based on these two variables. Illustrate your result in two-way sensitivity graph (10)

d)Can you decide whether to invest after these analyses? If not, what to do next?(5)

PLEASE USE R STUDIO AND NOT INVOLVE REGRESSION

In NZ supermarkets, the average weight of a banana is 118.6 grams. An agricultural scientist buys bananas from a supermarket. Their weight, in grams, is as follows: c(108.4, 125.9, 96.1, 136.6, 109.3, 116.4, 88.6, 79, 88) She suspects that this sample of bananas is lighter than average and wonders if this supermarket is selling bananas that are lighter than the NZ average.

(a) State a sensible null hypothesis

(b) State the precise definition of p-value and explain what "more extreme" means in this context

(c) Is a one-sided or two-sided test needed? justify

(d) Perform a student t-test using R and interpret

(e) Perform a Z test and account for any differences you find

PLEASE USE R STUDIO AND NOT INVOLVE REGRESSION

Question 1

Which one of the following methods can NOT be used for a numeric response variable?

Group of answer choices

Random Forest

Regression Tree

K nearest Neighbor

Classification Tree

Question 2

Given data, we get the following result for a logistic regression ln ( o d d s r a t i o ) = 2 x 1. what is the estimate of p (probability of success) at point x=0.10415

Group of answer choices

-0.7917

0.31

3.20

0.28

Consider the following problem. We want to n regression tree (with one split) on the NEG variable to predict the average GDP in each region (R1 and R2). The no split model is represented in the left graph.

Assume by one split at NEG <-10 vs NEG -10 we get the following fit mean output for each region. (The two graphs on the right)

Answer the following three questions.

Question 3

Calculate the R-sq of the NO SPLIT model

Group of answer choices

0.83

0

317.4083

1

Question 4

What is the amount of SSE of the one split model?

Group of answer choices

11.77819

114.51899

0

102.7408

Question 5

Calculate the SSE reduction based on the one split.

Group of answer choices

0

202.88931

114.51899

317.4083

Question 6

Calculate the R-sq of the one split model.

Group of answer choices

0

0.3608

0.6392

0.0371

Question 7

Given the following confusion matrix, Calculate the False Negative and True Negative rates respectively.

Group of answer choices

0.384,0.317

0.383,0.683

0.683,0.383

0.317,0.384

Question 8

Which of the following is TRUE?

Group of answer choices

Random Forest only chooses a random subset of variables to split on in each leaf node.

In JMP decision tree, when we have multiple predictors, we split on the variable with the smallest candidate SSE.

Each time wen random forest we get the same training R-sq value.

Both the Validation and Training R-sq will go up by each split added to the model because it creates a more accurate prediction.

Question 9

Given some operational conditions as predictor variables, we intend to predict if a machine is failed or not failed. Which of the following methods is not appropriate?

Group of answer choices

Classification Tree

Regression Tree

Random Forest

Logistic Regression

Question 10

120 out of 600 patients reported having allergies to a drug. What is the odds of NOT being allergic?

Group of answer choices

0.2

0.25

0.8

4

Question 11

Which one of the following statements is TRUE.

Group of answer choices

Trees are parametric methods because they consider a functional form for the prediction.

Trees are very flexible to fit not well behaving response variables.

Trees split on the response variable range and each leaf will cover only a certain range of the response variable.

Trees can only be used with continuous predictor variables.

Question 12

Given the following ROC curve, which model one is the worst model?

Group of answer choices

Test 1

Test 3

Test 2

Question 13

Consider the following regression tree. what is the prediction of the response variable when L T G 4.6052 and 24.4 < B M I < 27.8.

Group of answer choices

176.86486

159.743

74

208.57143

Question 14

Suppose quarterly sales was fitted with a seasonal model of 5*Q4+375 where Q4 is an indicator variable for the fourth quarter. What is the predicted sales for the second quarter?

Group of answer choices

0

5

375

380

Question 15

Suppose the best fitting model for a time series is y(t) = 12.4+0.25y(t-1). Suppose the observation values for the first 10 periods (read left to right) are 13,15,19,16,18,15,13,14,17,19

What is the prediction for period 12?

Group of answer choices

19

16.6875

15.9

17.15

Question 16

Which of the following is NOT true?

Group of answer choices

All regressions (time series and non-time series applications) should be followed up with an ACF on the residuals.

For a given time series, the PACF and ACF at lag 1 are equal.

For a given time series, the PACF and ACF at lag 1 are equal.

PACF is the correlation of a time series with its lags removing the effects of intervening lags.

Question 17

ACF cannot be used for determining what lags can serve as predictor variables in the regression model.

Group of answer choices

True

False

Question 18

Which one of the following models is an AR(2) model.

Group of answer choices

y(t) = b0 + b1*y(t-1)

y(t) = b0 + b1*t+b1*t squared

y(t) = b0 + b1*y(t-1)+b2* y(t-2)

y(t) = b0 + b1*t

Consider two different stocks, X and Y, with expected returns mX = 17.0% and mY = 14.9% and with standard deviations sX = 12.4% and sY = 10.0% for those returns. The two assets have a correlation, pr, of -0.7.

a. What is the covariance of the returns of stocks X and Y? (Hint: use the definition of the population correlation r to compute the covariance) In general, will constructing a portfolio from these two stocks reduce or increase the risk compared to the individual stocks? Briefly explain.

b. What is the expected return and standard deviation of a portfolio made up of stocks X and Y which is 20% stock X (the remainder stock Y)?

c. What is the expected return and standard deviation of a portfolio made up of stocks X and Y which is 50% stock X?

d. What is the expected return and standard deviation of a portfolio made up of stocks X and Y which is 80% stock X? e. Which of the portfolios above, either (b), (c), or (d), offers the best combination of risk and return? Briefly explain.

I am stuck on this problem set.

Consider the sample data on Boston house prices given by the Excel file "Boston.xlsx". The definitions of each column are given below

1. CRIM: per capita crime rate by town

2. ZN: proportion of residential land zoned for lots over 25,000 sq.ft.

3. INDUS: proportion of non-retail business acres per town

4. CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)

5. NOX: nitric oxides concentration (parts per 10 million)

6. RM: average number of rooms per dwelling

7. AGE: proportion of owner-occupied units built prior to 1940

8. DIS: weighted distances to five Boston employment centers

9. TAX: full-value property-tax rate per $10,000

10. PTRATIO: pupil (student) - teacher ratio by town

11. VALUE: Median value of owner-occupied homes in $10,000's

You have been asked to build a model that will help you estimate/explain the value of a given property in the Boston area given all the other variables (a total of 10 variables) given in the above list.

21) What is the R-Squared of your multiple linear regression model?

a) 0.72 b) 0.56 c) 0.66 d) 0.92

22) Obtain the variance inflation factor for the variable "crim" to test the existence of multi-collinearity.

a) 10 b) 2.9 c) 1.01 d) 1.56

23) What would be your estimate of a property in the Boston region for a property with the following characteristics?

crim

zn

indus

chas

nox

rm

age

dis

tax

ptratio

0.005

12

2.1

0

0.5

6

38

2

296

15.3

a) 23.5 b) 30.35 c) 23.7 d) 22.4

At a significance level of 0.01, check if the error terms of your multiple linear regression model are normally distributed (for Q24 and 25)?

24) What is the p-value of your test?

a) 0.85 b) 0 c) 0.01 d) 0.02

25) What is your conclusion?

a) Fail to reject the null hypothesis, the residuals are normally distributed.

b) Fail to reject the null hypothesis, the residuals are not normally distributed.

c) Reject the null hypothesis, the residuals are normally distributed.

d) Reject the null hypothesis, the residuals are not normally distributed.

1. Richard Miyashiro purchased a condominium and obtained a30-year loan of$199,000at an annual interest rate of8.25%. (Round your answers to the nearest cent.)

(a) What is the mortgage payment?

$

(b) What is the total of the payments over the life of the loan?

$

(c) Find the amount of interest paid on the mortgage loan over the30years.

$

2.) The Mendez family is considering a mortgage loan of$350,500at an annual interest rate of6.8%.

(a) How much greater is their mortgage payment if the term is 20 years rather than 30 years?

$

(b) How much less is the amount of interest paid over the life of the 20-year loan than over the life of the 30-year loan?

$

3.) After making payments of$907.10for8years on your 30-year loan at8.3%, you decide to sell your home. What is the loan payoff? (Round your answer to two decimal places.)

4.) A homeowner has a mortgage payment of$999.10, an annual property tax bill of$594, and an annual fire insurance premium of$290. Find the total monthly payment for the mortgage, property tax, and fire insurance. (Round your answer to the nearest cent.

1. A statistics practitioner took a random sample of 41 observations from a population whose standard deviation is 30 and computed the sample mean to be 103.

Note:For each confidence interval, enter your answer in the form (LCL, UCL). You must include the parentheses and the comma between the confidence limits.

A. Estimate the population mean with 95% confidence.

Confidence Interval =

B. Estimate the population mean with 90% confidence.

Confidence Interval =

C. Estimate the population mean with 99% confidence.

Confidence Interval =

2. The number of cars sold annually by used car salespeople is normally distributed with a standard deviation of 15. A random sample of 300 salespeople was taken and the mean number of cars sold annually was found to be 73. Find the 97% confidence interval estimate of the population mean.

Note:For each confidence interval, enter your answer in the form (LCL, UCL). You must include the parentheses and the comma between the confidence limits.

Confidence Interval =

3.A statistics practitioner took a random sample of 49 observations from a population whose standard deviation is 23 and computed the sample mean to be 96.

Note:For each confidence interval, enter your answer in the form (LCL, UCL). You must include the parentheses and the comma between the confidence limits.

A. Estimate the population mean with 95% confidence.

Confidence Interval =

B. Estimate the population mean with 95% confidence, changing the population standard deviation to 47;

Confidence Interval =

C. Estimate the population mean with 95% confidence, changing the population standard deviation to 8;

Confidence Interval =

4. Among the most exciting aspects of a university professor's life are the departmental meetings where such critical issues as the color the walls will be painted and who gets a new desk are decided. A sample of 20 professors was asked how many hours per year are devoted to such meetings. The responses are listed below. Assuming that the variable is normally distributed with a population standard deviation of 9 hours and that you can use the Z-Interval test, estimate the mean number of hours spent at departmental meetings by all professors. Use a confidence level of 94%.

Note:For each confidence interval, enter your answer in the form (LCL, UCL). You must include the parentheses and the comma between the confidence limits.

Confidence Interval =

5. It is known that the amount of time needed to change the oil on a car is normally distributed with a standard deviation of 5 minutes. A random sample of 95 oil changes yielded a sample mean of 28 minutes. Compute the 92% confidence interval estimate for the population mean.

Note:For each confidence interval, enter your answer in the form (LCL, UCL). You must include the parentheses and the comma between the confidence limits.

Confidence Interval =